SOTAVerified

Fairness

Papers

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Showing 1126–1150 of 5676 papers

TitleStatusHype
Dynamic Scheduling Strategies for Resource Optimization in Computing Environments—0
Antenna Health-Aware Selective Beamforming for Hardware-Constrained DFRC Systems I—0
Fair Knowledge Tracing in Second Language Acquisition—0
Examining Imbalance Effects on Performance and Demographic Fairness of Clinical Language Models—0
ELEVATE-GenAI: Reporting Guidelines for the Use of Large Language Models in Health Economics and Outcomes Research: an ISPOR Working Group on Generative AI Report—0
Constructing Fair Latent Space for Intersection of Fairness and Explainability—0
Fair and Accurate Regression: Strong Formulations and Algorithms—0
Where Did Your Model Learn That? Label-free Influence for Self-supervised LearningCode0
Fairness in Reinforcement Learning with Bisimulation Metrics—0
Does calibration mean what they say it means; or, the reference class problem rises again—0
Towards Environmentally Equitable AI—0
Data-Driven Fairness Generalization for Deepfake Detection—0
A Systems Thinking Approach to Algorithmic Fairness—0
FAP-CD: Fairness-Driven Age-Friendly Community Planning via Conditional Diffusion GenerationCode0
FairDD: Enhancing Fairness with domain-incremental learning in dermatological disease diagnosis—0
Improving Equity in Health Modeling with GPT4-Turbo Generated Synthetic Data: A Comparative Study—0
FairREAD: Re-fusing Demographic Attributes after Disentanglement for Fair Medical Image Classification—0
Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative?—0
Decoding fairness: a reinforcement learning perspectiveCode0
FROC: Building Fair ROC from a Trained ClassifierCode0
Movie2Story: A framework for understanding videos and telling stories in the form of novel text—0
Operationalising Rawlsian Ethics for Fairness in Norm-Learning Agents—0
Towards Friendly AI: A Comprehensive Review and New Perspectives on Human-AI Alignment—0
On Calibration in Multi-Distribution Learning—0
From Expectation to Habit: Why Do Software Practitioners Adopt Fairness Toolkits?—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)99.86—Unverified
21D-CSNNPredictive Equality (age)97.8—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)96.87—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)98.97—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)98.45—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)98.68—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)99.31—Unverified
#ModelMetricClaimedVerifiedStatus
1Neighbour LearningDegree of Bias (DoB)0.49—Unverified
#ModelMetricClaimedVerifiedStatus
1Neighbour LearningDegree of Bias (DoB)6.26—Unverified
#ModelMetricClaimedVerifiedStatus
1Neighbour LearningDegree of Bias (DoB)1.96—Unverified